Our work was precisely in optimization over massive numbers of possible scenarios amid multi-dimensional uncertainties. Again, significantly outperforms LP. Not surprised that you are an advocate of math optimization. Most people working on optimization in industrial operations and supply chains have an OR background, and this sort of conservatism is common. But there are lots of situations that LP can't optimize while RL can.
"Again, significantly outperforms LP."
No, this is mathematically impossible. You cannot out-optimize a mathematical global optimization solver. Whether that is LP, MILP, or NLP. Exactly because they provide a certificate of optimality.
"But there are lots of situations that LP can't optimize while RL can"
I will be satisfied if you just show us 1.
Stay in your lane, and state that you built a fast heuristic that works in the problems you tried. Nobody can dispute this. But don't make claims you cannot support.
The main challenge is to optimize these decisions. With optimality guarantees. Consistently. Day in, day out, with various uncertainties.
That is what math optimization has been doing for decades.
And no, RL is nowhere to be seen in production, except from some consulting projects that are abandoned.